Journal of Economics, Finance and Accounting Studies
Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
Al-Kindi Center for Research and Development
Data-Driven Risk Detection in Digital Financial Systems: Implications for Financial Crime Prevention and Infrastructure Resilience
The increasing digitalization of financial systems has transformed how financial institutions process transactions, deliver services, and manage operational risk. However, the rapid growth of electronic payment systems and online financial platforms has also increased exposure to fraud, identity theft, and cyber-enabled financial crime. This study explores the relationship between the expansion of the digital financial system and financial crime exposure in the United States, using publicly available data from the Federal Trade Commission (FTC) Consumer Sentinel Network and the Federal Reserve Payments Study. Adopting an exploratory and policy-oriented approach, the study uses descriptive trend analysis, comparative visualization, and conceptual financial risk interpretation to examine evolving fraud patterns and digital transaction vulnerabilities. The findings indicate that the growth of digital payment infrastructures has been accompanied by persistently high levels of fraud reports, identity theft incidents, and financial losses, particularly within credit card and electronic transaction environments. In response, the study proposes a conceptual Data-Driven Risk Detection Framework integrating transaction analytics, anomaly detection, compliance monitoring, and adaptive learning mechanisms to strengthen financial crime prevention and infrastructure resilience. The study contributes to the literature by offering a practical, infrastructure-focused perspective on data-driven financial risk governance in increasingly digital financial ecosystems.
Digital Financial Systems; Financial Crime Prevention; Fraud Detection; Financial Risk Analytics; Data-Driven Risk Detection; Financial Infrastructure Resilience
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
The Impact of New Quality Productive Forces on Corporate Environmental Performance: A Dual Perspective Based on Green Innovation and Environmental Information Disclosure
In the era of coordinated advancement of the “dual carbon” goals and high-quality development, how to transform new quality productive forces into tangible environmental governance outcomes has become a core issue in enterprises’ green transformation. This study takes Chinese A-share listed companies from 2015 to 2024 as the research sample and systematically examines the impact of new quality productive forces on corporate environmental performance, as well as its underlying mechanisms and heterogeneous characteristics. To address potential endogeneity issues, the instrumental variable approach and propensity score matching method are employed. The baseline regression results show that an increase in the level of new quality productive forces significantly improves corporate environmental performance. The conclusion remains robust after a series of robustness checks, including replacing the explanatory variable, changing the regression model, excluding samples from municipalities, and adjusting the clustering level. Mechanism tests reveal that new quality productive forces exert their effects primarily through two pathways: first, by stimulating enterprises’ green technological innovation vitality and promoting the output of green invention patents, thereby providing technological support for environmental improvement; second, by enhancing the quality of environmental information disclosure, which increases the transparency and supervisability of corporate environmental behavior and forms a combined internal and external supervisory force that compels continuous improvement. Heterogeneity analysis indicates that the positive environmental effect of new quality productive forces is more pronounced in enterprises with high profitability, in high-tech industries, and in heavily polluting industries, while the effect is relatively weaker in enterprises with low profitability, in non-high-tech industries, and in lightly polluting industries. This study not only theoretically expands the cognitive boundaries of the environmental effects of new quality productive forces and reveals the dual transmission mechanisms of green innovation and environmental information disclosure, but also provides practical references for enterprises to cultivate new quality productive forces in a differentiated manner according to their own resource endowments, thereby promoting green transformation.
New quality productive forces; corporate environmental performance; level of green innovation; environmental information disclosure quality
Education Outcomes and Structural Constraints in Côte d’Ivoire: A Political Economy Analysis of Human Capital Formation
This study examines the relationship between educational investment, system efficiency, and human capital formation in Côte d’Ivoire within a political economy framework. While human capital theory suggests that increased investment in education promotes productivity and economic growth, evidence from developing economies indicates that educational expansion alone does not necessarily generate improved developmental outcomes. The study aims to analyze how institutional quality, governance structures, labor market alignment, and structural constraints influence the effectiveness of educational investments in Côte d’Ivoire. The research adopts a qualitative analytical approach based on a structured review of academic literature, development reports, and policy documents from institutions including the World Bank, UNESCO, UNICEF, and the African Development Bank. The analysis integrates perspectives from human capital theory, endogenous growth theory, and institutional economics to evaluate the interaction between educational access, learning quality, and socio-economic outcomes. Findings indicate that although Côte d’Ivoire has achieved substantial progress in expanding access to education, major challenges persist in terms of learning outcomes, resource efficiency, labor market mismatch, demographic pressure, and institutional capacity. The study demonstrates that the developmental returns of education depend not only on financial investment but also on governance quality, policy coordination, and the alignment between educational systems and economic structures. The paper contributes to the literature by providing an interdisciplinary interpretation of education outcomes in a developing-country context and by emphasizing the limitations of input-centered education policies. It concludes that sustainable human capital formation requires reforms focused on education quality, institutional effectiveness, accountability, and labor market relevance rather than access expansion alone.
human capital formation; education policy; institutional efficiency; political economy; Côte d’Ivoire; governance; labor market alignment
Electrifying Healthcare Logistics: The Role of Electric Vehicle Transportation in Optimizing Pharmaceutical Value Chains, Digital Marketing, and Financial Performance
The transformation of healthcare logistics through electric vehicle (EV) adoption represents a critical advancement in optimizing pharmaceutical value chains. This study examines the integration of EV transportation systems within healthcare distribution networks and evaluates their impact on operational efficiency, digital marketing capabilities, and financial performance. By synthesizing insights from supply chain analytics, artificial intelligence, and sustainable mobility frameworks, the research identifies key drivers, barriers, and outcomes associated with electrified logistics. The findings suggest that EV-enabled logistics not only reduce environmental impact but also enhance delivery reliability, improve patient-centric service models, and support data-driven marketing strategies. Furthermore, financial analysis indicates long-term cost advantages despite high initial investment, positioning EV logistics as a strategic asset in modern healthcare ecosystems.
Electric Vehicles, Healthcare Logistics, Pharmaceutical Supply Chain, Digital Marketing, Financial Performance, Sustainability, AI Integration
Economic Resilience of Immunization Systems: Development and Validation of the ERIIS Index
The health and economic benefits of vaccination programs are tremendous, yet the financial sustainability will be vulnerable to macroeconomic crises, change of donors and government budget constraints. Current research calculates the health impact and cost-effectiveness and does not determine the economic resilience at the system level. In this paper, Economic Resilience Index of Immunization Systems (ERIIS), a composite and multi-dimensional index, is introduced; the index is designed to validate and forecast the ability of a country to maintain immunization outcomes in the case of fiscal shock. ERIIS is a composite score of five pillars: fiscal autonomy, health economic integration, procurement efficiency, funding diversification and data and forecast capacity into 100 points. According to the model, there are measurable indicators, weighting process, and a validation method that are to be employed in an empirical manner. ERIIS can provide a forecasting tool to policymakers because operationalizing economic resilience can be used to determine vulnerability, improve financial preparedness, and reform sustainable immunszation.
Economic resilience, Immunization systems, Vaccine financing, Health system resilience, Public financial management, Immunization economics, Vaccine procurement, Health policy
Adoption of Seventeen SMA Tools in the Saudi Sectors: A Contemporary Analysis
This study investigates the adoption of seventeen strategic management accounting tools (SMAT) across Saudi service sector organizations, integrating technological, organizational, and strategic determinants within a unified TOE–TAM–SMAT–Digital Capability framework. Using a quantitative, cross-sectional design, data were collected from accountants and managers representing diverse service industries, enabling a comprehensive assessment of adoption patterns and the influence of demographic and organizational characteristics. The findings reveal that competitor-oriented tools—particularly benchmarking and competitive position monitoring—are the most widely adopted, reflecting the sector’s emphasis on external market intelligence and strategic positioning. Customer oriented tools exhibit moderate adoption, with strategic pricing and customer profitability analysis leading the category, while advanced customer analytics remain underutilized. Cost management tools show selective but meaningful uptake, with value chain costing and life cycle costing demonstrating the highest usage. Demographic variables such as company size, service type, listing status, and managerial experience exert minimal influence on adoption, indicating that structural characteristics play a limited role. Instead, digital capability, SMAT orientation, and market position emerge as the primary drivers shaping adoption pathways. The moderating effect of market position strengthens the relationship between digital capability and adoption, while SMAT orientation mediates the influence of digital capability on cloud accounting usage. Reliability and validity analyses confirm the robustness of the measurement instrument. Overall, the study provides empirical evidence that SMAT adoption in the Saudi service sector is driven by strategic imperatives and digital readiness rather than demographic factors. The findings highlight the need for enhanced digital and analytical capabilities to support the adoption of advanced SMAT, particularly those requiring long term customer insight and sophisticated cost analytics. The study contributes to the understanding of SMA in digitally transforming service economies and offers practical implications for managers, policymakers, and educators seeking to strengthen strategic decision making and performance measurement practices.
Cloud Accounting; TOE Framework; TAM; SMAT Orientation; Digital Capability; Market Position; Competitive Intelligence; Strategic Agility; Cost Efficiency; Technology Adoption
Electric Vehicles and Healthcare Efficiency: A Critical Perspective on Cost, Access, and Environmental Impact
The intersection of Electric Vehicles (EVs) and healthcare efficiency presents a complex landscape where environmental sustainability, economic viability, and public health outcomes converge. While the transition to EVs is often framed as a purely environmental or automotive shift, its implications for healthcare infrastructure and patient access are profound. This paper critically examines how EV adoption in healthcare—particularly in logistics, emergency response, and patient transportation—affects cost structures, service accessibility, and sustainability outcomes. By integrating insights from digital health technologies, artificial intelligence, and evolving consumer expectations, this study highlights both the opportunities and systemic challenges of electrifying healthcare infrastructure in the United States.
Good Corporate Governance, Financial Performance, Rural Banks.
Client Happiness as a Predictive Economic Variable in Revenue Systems
In the turbulent environment of the contemporary revenue management systems, the conventional client satisfaction indicators, such as Net Promoter Scores, are less predictive as they do not rely on prospective behavioral factors. This study redefines client happiness as an economic variable with measurement, and puts forward a new composite measure, the Emotional Continuity Index (ECI) a new model of emotionally stable, which predicts repeat engagement, quick decision making and retention. ECI is integrated into Customer Happiness Intelligence System (CHIS), a patent-pending framework based on practical executive practice, which gathers operational data through CRM systems, transaction records, and feedback systems and compiles them with the help of machine learning algorithms in real time to deliver a picture of customer satisfaction levels. The research notes the effectiveness of ECI based on a mixed-methodology which involves conceptual modeling, weighted algorithmic formulation, and 12 months of empirical validation using a stratified sample of 749 clients of Sunlocate Properties (commercial real estate sector): Results show 22% increase in revenue predictability, 15% decrease in churn, and 18% faster decision-making. Such results place client happiness as a measurable managerial dial, and this raises the strength of enterprises in complicated settings. Commercial intelligence and customer-centric design implications, which promote structured analytics to develop flexible, scalable business, can be implied. The piece of writing adds a coherent intellectual journey between the executive experience and technological innovation to close the gaps in the theoretical background and the real-life applications to make the best decisions.
Client happiness, revenue predictability, Emotional Continuity Index, commercial intelligence, customer lifecycle management, behavioral analytics
Driving the Future of Healthcare: Electric Vehicles, Data-Driven Marketing, and Financial Innovation in Pharmaceutical Value Chains
The healthcare and pharmaceutical industries are undergoing a transformative shift driven by electric vehicle (EV) adoption, data-driven marketing, and financial innovation. This paper examines how EV integration in pharmaceutical logistics enhances sustainability, reduces delivery costs, and improves supply chain resilience. It further explores how AI-powered marketing systems and blockchain-based financial mechanisms are reshaping customer engagement and operational transparency. The study synthesizes prior research to present a multi-dimensional framework linking EV infrastructure, digital intelligence, and financial optimization in healthcare value chains.
Innovation, Decision-Making, LOTAR Model, Reflective Learning, Cognitive Psychology, Qur’anic Epistemology, Creativity, Educational Management.
Internal Audit Practices, Management Controls and Fraud Prevention among Public Technical and Vocational Education and Training Institutions in Nairobi Metropolitan Area, Kenya
This study examined the effect of internal audit practices and management controls on fraud prevention among public Technical and Vocational Education and Training institutions in the Nairobi Metropolitan Area, where persistent financial mismanagement raises concerns about audit effectiveness using a descriptive research design. The study targeted 141 institutions, with 103 heads of internal audit as respondents. Primary data were collected using structured questionnaires that satisfied validity and reliability requirements. Data were analyzed using correlation and multiple regression analyses. Results from the study, which was anchored on Agency Theory, Fraud Triangle Theory, and Institutional Theory, show strong positive associations between fraud prevention and internal control systems, management controls, and internal audit IT adoption. Regression results indicate that internal audit IT adoption is the most influential predictor of fraud prevention, followed by internal control systems, both of which are statistically significant. The model explains a substantial proportion of variation in fraud prevention. The moderating effect of board oversight is not statistically significant. The study concludes that strengthening internal audit systems, particularly through digital audit adoption and robust internal controls, significantly enhances fraud prevention in public TVET institutions. Management controls further reinforce accountability.
Internal audit practices, internal control systems, fraud prevention, management controls
Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
A Flexible Elasticity Framework for Measuring Firm Internationalization: Theoretical Model and Simulation-Based Evidence
This study develops a flexible elasticity framework for measuring firm internationalization. Unlike standard composite indices based on fixed weights, we model internationalization as a heterogeneous elasticity function in which the contribution of each dimension varies across firms. The model generalizes traditional additive approaches by introducing firm-specific elasticities that depend on observable characteristics of the firms. We provide a theoretical characterization of the model and evaluate its properties using simulations. The results show that fixed-weight indices systematically compress heterogeneity and distort firm rankings, whereas the proposed framework preserves structural variation and provides a more consistent representation of internationalization.
Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model
In contemporary times, decision-making has become one of the most essential managerial, educational, and social competencies. While decisions occur frequently, they become meaningful and transformative only when they embody elements of creativity and innovation. The fundamental question, therefore, is: Where does innovation in human decision-making originate, and what activates this process within the individual? This study, entitled “Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model,” aims to provide a scientific and integrative explanation of innovative decision-making by drawing on cognitive theories and Qur’anic epistemology. Inspired by these principles and integrated with established cognitive frameworks, the proposed LOTAR Model introduces a five-stage dynamic process consisting of Listening, Observation, Thinking, Action, and Reflection. The model argues that innovation does not arise from sudden inspiration, but from the continuous interaction among perception, cognition, experience, and reflective learning. A controlled field experiment was conducted within the Department of Management at Takhar University using a real educational task—improving classroom cleanliness. The results indicated that the LOTAR framework enhances innovation by strengthening Thinking, Action, and Reflection. The findings demonstrate that LOTAR provides a scientifically grounded, culturally contextualized, and Qur’an-informed approach for enhancing reflective thinking and innovative decision-making.
Investigating Effective Factors in Application of the Artificial Intelligence in Iran's Food Industry and its Effects on Employment
The fourth industrial revolution has brought tremendous changes with the introduction of artificial intelligence to various industries. In this study, the effective factors in the use of artificial intelligence in Iran's food industry and its effects on employment have been identified. Due to the growing need to improve the quality of food products, increase productivity, and reduce costs, the use of new technologies, including artificial intelligence, in this industry seems inevitable. This research examines the opportunities and challenges in the use of artificial intelligence and analyzes its effects on employment in Iran's food industry. Investigating these factors in Iran's food industry is of great importance and necessity, and increases productivity, changes in employment, competitiveness, research and development, environmental sustainability, improves food security, and develops appropriate policies. The general goal of investigating these effective factors in the use of artificial intelligence in Iran's food industry is to create a stable and innovative ecosystem that will lead to the improvement of product quality, increase employment, and the realization of sustainable development goals. The type of this research in terms of practical purpose, in terms of descriptive-analytical execution method, and in terms of data collection using studies published until 2023 using PubMed, Cochrane, SCOPUS databases, and Google Scholar search engine with key related words have been searched. This research was selected after a complete review of 30 relevant articles that were consistent with the purpose of the present study. The results show that while artificial intelligence can reduce some traditional jobs, it also creates new job opportunities and increases the need for new skills. In this research, we found that workforce skills and technological infrastructure are of higher importance, government support and organizational culture are of high importance, and costs and benefits are of medium importance, and each of them creates its own effects on employment. Finally, this research provides recommendations to policymakers and the food industry to facilitate the transition process towards artificial intelligence.
Employment, Iran, Artificial Intelligence, Food Industry, Productivity, New Technologies
Redefining Healthcare Consumption: AI-Driven Buyer Persona and Financial Transformation
Artificial Intelligence (AI) is revolutionizing the healthcare industry by reshaping consumer behavior and transforming financial structures. This study examines the evolution of the future healthcare consumer buying persona and evaluates the financial implications of AI integration. The findings suggest that AI-driven personalization, predictive analytics, and digital engagement tools significantly influence consumer decision-making while enhancing operational efficiency and reducing costs for healthcare providers. Additionally, technologies such as blockchain and digital twin systems contribute to transparency and supply chain optimization. The study concludes that AI is fostering a more consumer-centric and financially sustainable healthcare ecosystem.
Artificial Intelligence, Healthcare Consumer Behavior, Financial Impact, Digital Health, Personalization, Predictive Analytics
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Intelligent Pay-by-Bank Architecture: Fraud-Aware Analytics and Risk Modeling for U.S. Account-to-Account Payment Systems
Pay-by-bank is moving from a niche alternative to a strategically important layer in the U.S. payments landscape. Its appeal is clear: direct account-to-account (A2A) transfers can reduce merchant acceptance costs, accelerate funds availability, support recurring and bill-payment use cases, and create a pathway for open-banking-enabled checkout experiences that do not depend on card rails. At the same time, the same structural strengths that make pay-by-bank attractive—irrevocability, speed, richer data exchange, API connectivity, and multi-party orchestration—also create distinctive fraud, operational, and consumer-protection risks. Unlike card networks, many pay-by-bank implementations cannot rely on mature chargeback mechanisms as a primary safety valve, which makes ex ante risk scoring, identity assurance, behavioral analytics, and post-transaction monitoring more important. This paper develops a framework for an intelligent pay-by-bank architecture for the United States, integrating fraud-aware analytics, explainable machine learning, graph-based entity resolution, sanctions and AML screening, and rail-aware control design across ACH, Same Day ACH, RTP, and FedNow-enabled flows. Using official U.S. sources and contemporary scholarly research, the paper synthesizes the structural drivers of A2A growth, maps the major fraud typologies that threaten consumer and merchant adoption, and proposes a layered analytic design that joins customer risk, payment context, device and channel telemetry, open-banking consent events, account validation, and network intelligence into a unified decision stack. The paper also advances a practical governance model centered on explainability, model-risk management, fairness, vendor oversight, and escalation workflows aligned to high-stakes payment decisions. The discussion argues that successful U.S. pay-by-bank adoption depends not only on rail availability but on trusted orchestration: institutions must combine real-time controls, adaptive thresholds, human review for ambiguous cases, and continuous learning from returns, disputes, fraud reports, and customer complaints. The manuscript concludes that intelligent pay-by-bank systems can materially improve the safety and efficiency of U.S. A2A payments when technical architecture, controls, and governance are designed together rather than sequentially.
Pay-by-bank, account-to-account payments, ACH, Same Day ACH, FedNow, fraud analytics, explainable AI, open banking, synthetic identity, U.S. payment systems
Clustering Capital Management Approaches: Evidence from Banking Firms during Economic Disruptions
The COVID-19 pandemic significantly disrupted global markets, supply chains, and business operations, leading to adverse effects on firm performance and economic stability. The banking sector, in particular, was compelled to adapt its capital management strategies to remain resilient amid financial uncertainty. Effective capital management—encompassing efficiency, liquidity, and solvency—became critical, as poor financial decisions during crisis periods can threaten organizational survival. This study aims to examine how banking firms respond to financial uncertainty through their capital management practices and to determine how these responses relate to firm profitability. Specifically, it seeks to identify distinct patterns of capital management and assess their implications for financial performance. A quantitative research design was employed using survey data from bank representatives. K-Means Cluster Analysis, utilizing the Lloyd algorithm, was applied to classify firms based on efficiency, liquidity, and solvency. The findings revealed three distinct clusters: efficient-solvent, high-liquid, and safe-player. Results further indicate a significant relationship between capital management approaches and profitability, highlighting the importance of balancing efficiency, liquidity, and solvency. This study contributes to the literature by offering an integrated clustering perspective on capital management and provides practical insights for banking institutions to develop more resilient financial strategies during periods of economic uncertainty.
Capital management, business uncertainty, profitability, efficiency, liquidity, solvency
Modeling the Consumer Transition from Cash to Digital Payments and Its Implications for Financial System Efficiency, Transparency, and Inclusion
This study develops a quantitative framework for modeling the consumer transition from cash to digital payments and for evaluating how that transition affects efficiency, transparency, and financial inclusion. The paper combines evidence from the Federal Reserve’s Diary and Survey of Consumer Payment Choice, the World Bank’s Global Findex Database, Bank for International Settlements research on fast payment systems, and related studies published. The empirical motivation is clear: cash remains important, but U.S. consumers increasingly rely on cards, mobile phones, online banking, and account-to-account rails for purchases, bills, and person-to-person transfers. To interpret this shift, the paper specifies a multi-stage adoption framework in which consumers select payment instruments according to transaction size, acceptance, security, convenience, costs, record keeping, liquidity constraints, and digital access. A transition index and payment-efficiency score are linked to transparency and inclusion outcomes through a comparative design and a U.S.-focused calibration exercise. The results show that movement from cash toward digital payments is neither linear nor uniform. Digital migration is fastest where transaction records, remote commerce, and instant settlement offer visible utility gains. Yet adoption slows for cash-dependent households, privacy-sensitive users, the underbanked, and populations with weaker access to smartphones, broadband, or low-fee accounts. The analysis indicates that payment digitalization improves speed, traceability, and operating efficiency, but inclusion gains depend on governance, affordability, fraud protection, and user-centered design. The main policy implication is that an efficient payment future is not cashless by default; it is plural, interoperable, transparent, and resilient.
Cash, digital payments, financial inclusion, payment systems, consumer behavior, cards, mobile payments, fast payments, transparency, payment efficiency
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Predictive Modeling for Detecting Suspicious Trading Patterns, Anomalous Order Activity, and Market Manipulation in U.S. Equity Markets
Modern U.S. equity surveillance increasingly depends on predictive systems that flag suspicious order placement, cross-venue quote behavior, and abnormal execution patterns before manipulative episodes fully unfold. This paper develops an AI-driven research framework for detecting suspicious trading patterns, anomalous order activity, and market manipulation in U.S. equity markets by combining market microstructure theory, public regulatory evidence, and contemporary machine-learning methods. Drawing on more than forty professional sources, the study positions surveillance as a multimodal prediction problem spanning limit-order-book dynamics, order-routing behavior, venue fragmentation, trader interaction networks, news and disclosure context, and enforcement-informed labels. Market-structure evidence shows that U.S. equity average daily volume rose from an estimated 11.1 billion shares in 2023 to 12.2 billion in 2024 and 17.6 billion in 2025, while off-exchange Trade Reporting Facility share increased from about 45% in April 2023 to 47.0% in 2024 and 50.6% in 2025, underscoring the scale and fragmentation challenge facing surveillance teams. The paper proposes hybrid architecture that integrates gradient-boosted baselines, temporal transformers, and graph neural networks with explainable AI overlays for analyst review. Methodologically, the framework emphasizes severe class imbalance, regime shifts, weak and delayed labels, and cost-sensitive evaluation anchored in precision at alert budgets, time-to-detection, and economic materiality rather than headline accuracy alone. The paper argues that trustworthy surveillance requires predictive power, governance discipline, auditable features, and human escalation aligned with U.S. market-structure reforms. The result is a blueprint for more adaptive and useful market-abuse detection.
market manipulation, spoofing, layering, suspicious trading, anomalous order activity, graph neural networks, market surveillance, U.S. equities, explainable AI, weak supervision
Socio-Economic Impacts and Fiscal Vulnerabilities Following the February 6, 2023 Kahramanmaraş Earthquakes in Türkiye
The two major earthquakes (Mw 7.7 and Mw 7.6) that occurred on 6 February 2023, centered in Kahramanmaraş, produced profound socio-economic impacts in southeastern Türkiye. The disaster directly affected eleven provinces, caused widespread destruction, and resulted in more than 50,000 fatalities, while also leading to significant losses in Syria. Approximately 14 million people were directly affected by the physical destruction, and long-term socio-economic consequences are expected to persist. This study evaluates the direct and indirect economic losses caused by the earthquakes within a comprehensive framework, focusing on their impact on production, foreign trade, labor markets, and public finance. In this context, the Adaptive Regional Input–Output (ARIO) model, which enables time-dependent analysis of post-disaster economic impacts, is employed. The findings indicate that the earthquakes weakened regional production capacity, increased employment losses, and significantly expanded public expenditures, thereby deepening fiscal vulnerabilities. Population mobility has also led to structural changes in labor markets and national economic balances. Although reconstruction activities may support short-term recovery, sustainable growth requires integrated policy strategies that strengthen fiscal resilience, restore employment, and enhance long-term regional economic resilience and stability.
Socio-economic impacts, fiscal resilience, ARIO model, economic vulnerability
AI-Driven Predictive Analytics Framework for Anti-Money Laundering Risk Management and Financial Infrastructure Protection in U.S. Banking Systems
U.S. banks run anti money laundering (AML) programs that must detect suspicious activity at scale, document decisions, and file timely Suspicious Activity Reports (SARs) while protecting SAR confidentiality (31 C.F.R. § 1020.320, 2023; FFIEC, 2021). Illicit finance increasingly exploits payment rails and multi account networks, raising compliance exposure and operational risk (FinCEN, 2021). This manuscript proposes a compliance aware, AI driven predictive analytics framework for U.S. banking. The framework integrates three layers: supervised alert ranking and suppression using calibrated gradient boosted trees (Chen & Guestrin, 2016); unsupervised anomaly detection for emerging typologies under label lag (Liu et al., 2008); and graph learning to capture relationship centered laundering structures such as mule rings and layering (Kipf & Welling, 2017; Hamilton et al., 2017). We specify a data and feature pipeline for deposits, wires, ACH, cards, and instant payments, including preprocessing, entity resolution, feature engineering, and class imbalance controls using cost sensitive objectives and precision recall analysis (Elkan, 2001; Saito & Rehmsmeier, 2015). Explainability is embedded as an evidence packet for investigators and validators using SHAP, LIME, counterfactuals, and graph explanations (Lundberg & Lee, 2017; Ribeiro et al., 2016; Wachter et al., 2018; Ying et al., 2019). Privacy preserving options, including federated learning, secure aggregation, and differential privacy, enable cross portfolio learning without centralizing sensitive customer data (Dwork et al., 2006; McMahan et al., 2017). Deployment guidance addresses real time scoring, latency targets, drift monitoring, and integration with case management and BSA e filing.
i-Money Laundering, Machine Learning, Graph Neural Networks, Financial Crime, National Security, Explainable AI, Privacy-Enhancing Technologies
Design and Implementation of an AI-Augmented Autonomous Financial Operations Framework for Cloud-Native ERP Systems Using SAP BTP and RAP
Enterprise financial systems are undergoing rapid transformation due to increasing regulatory complexity, high-volume digital transactions, and the shift toward cloud-native architectures. Traditional ERP financial modules rely heavily on rule-based validations and manual reconciliation processes, limiting their ability to detect anomalies, prevent revenue leakage, and adapt dynamically to evolving compliance requirements. Despite the robustness of platforms such as SAP S/4HANA, financial operations in large enterprises continue to depend on static validations, post-period reconciliation, and manually supervised exception handling. These constraints increase fraud exposure, revenue leakage risk, and financial close cycle duration. This research introduces the Autonomous Financial Operations Framework (AFOF), a cloud-native, AI-augmented architecture that embeds intelligent automation directly within ERP transactional workflows. The framework integrates: The extensibility and microservices capabilities of SAP Business Technology Platform Behavior-driven service modeling via SAP ABAP RESTful Application Programming Model (RAP) Embedded machine learning services for anomaly detection and predictive analytics The proposed framework leverages the extensibility of SAP S/4HANA, the cloud capabilities of SAP Business Technology Platform, and the service-oriented programming paradigm of SAP ABAP RESTful Application Programming Model (RAP) to create a self-optimizing financial operations layer. The architecture introduces five core components: An Intelligent Posting Validation Engine using behavior-driven RAP logic. An AI-based Anomaly Detection Module for financial irregularities. Automated reconciliation services for high-volume subledger environments. Predictive revenue leakage analytics tailored for subscription-based monetization systems. A secure event-driven extension layer supporting scalable enterprise integration. A controlled enterprise-scale simulation was conducted using synthetic financial datasets modeling high-volume subscription billing environments (10–50 million monthly transactions). Comparative benchmarking against traditional rule-based ERP controls demonstrated: 43% reduction in financial close cycle duration 37% improvement in anomaly detection precision 52% reduction in manual reconciliation effort 28% decrease in revenue leakage exposure 31% faster exception resolution turnaround time Latency measurements confirmed that embedded AI validation introduced less than 8 ms average transactional overhead, preserving ERP performance integrity. Unlike conventional ERP enhancements that operate as external monitoring tools, the AFOF embeds machine learning–assisted controls directly within transactional workflows. This approach enables real-time compliance validation, adaptive financial risk scoring, and automated exception resolution, significantly reducing operational overhead and financial exposure. Performance modeling demonstrates measurable improvements in financial close cycle time, anomaly detection accuracy, and reconciliation efficiency when compared to legacy rule-based systems. This research contributes a scalable and reusable architectural model for AI-enabled financial automation in regulated industries, including telecommunications, digital commerce, healthcare billing and large-scale enterprise services. By integrating intelligent automation within mission-critical ERP systems, this research advances enterprise cybersecurity resilience, financial governance, and digital economic infrastructure modernization.
Design; Implementation; AI-Augmented Autonomous Financial Operations Framework; Cloud-Native ERP Systems; SAP BTP; RAP
Generative AI and U.S. Financial Reporting Integrity: Detecting Narrative Manipulation, Risk Disclosure Gaming, and Fraud Signals in 10-K Filings
U.S. capital markets rely on high-integrity corporate disclosure, yet the narrative portions of annual reports—particularly Management’s Discussion and Analysis (MD&A) and Risk Factors—remain vulnerable to strategic language management and, increasingly, generative-AI-assisted drafting. Unlike traditional misstatement detection that focuses on accounting ratios, narrative manipulation can distort investor beliefs through selective emphasis, obfuscation, boilerplate recycling, and inflated forward-looking language while remaining difficult to audit at scale. This paper develops a conceptual–methodological framework to measure disclosure integrity and detect narrative manipulation in U.S. 10-K filings. We construct a transparent EDGAR pipeline that parses MD&A and Risk Factors and extracts auditable features capturing (i) financial-context tone and uncertainty, (ii) readability and complexity, (iii) risk-factor novelty versus boilerplate drift (year-to-year similarity), (iv) forward-looking intensity, and (v) “tone–fundamentals gaps” that flag potential inconsistency between narrative claims and contemporaneous performance signals. We benchmark interpretable statistical models (logit/elastic net) against flexible machine-learning specifications while preserving explainability, and we evaluate performance using governance-relevant outcomes including enforcement-linked misstatement events and restatement proxies, complemented by market-based reactions around filing dates. We further test whether narrative-risk signals strengthen in the period associated with broad diffusion of generative AI writing tools. The proposed framework contributes to accounting, finance, and regulatory technology by converting narrative disclosure risk into measurable, monitorable indicators. Findings are positioned to inform SEC disclosure oversight, audit planning, and issuer governance by enabling scalable detection of narrative manipulation and risk-disclosure gaming that may undermine investor protection and U.S. market integrity.
10-K; MD&A; risk factors; disclosure integrity; textual analysis; narrative manipulation; generative AI; SEC enforcement; audit analytics; market integrity
FinTech for Climate Resilience: Measuring Insurance Gaps, Mortgage Stress, and Household Credit Risk in the United States
Climate-related hazards are increasingly transmitted into household balance sheets through the availability, affordability, and design of homeowners insurance. When premiums rise sharply, deductibles widen, or policies are non-renewed, households face larger out-of-pocket repair burdens and liquidity shocks that can elevate mortgage delinquency and broader consumer credit stress. These dynamics are not only a household welfare concern but also a national-interest issue for U.S. housing finance resilience: persistent insurance gaps can weaken loan performance, intensify regional housing volatility, and strain servicing and loss-mitigation capacity. This paper proposes a FinTech-oriented measurement and governance framework for climate resilience that links (i) county-level hazard exposure, (ii) insurance market frictions and coverage gaps, and (iii) mortgage and household credit outcomes in USA. Conceptually, the framework treats insurance gaps as a core mechanism of climate-to-credit transmission while positioning digital finance tools—InsurTech risk signals, embedded insurance/repair financing, and data-driven hardship identification—as levers to reduce amplification and improve consumer protection. Methodologically, we outline a public-data empirical strategy combining dynamic event studies around major hazard years with difference-in-differences designs exploiting heterogeneous insurance-supply tightening (e.g., non-renewal intensity and premium acceleration). We specify a replicable construct set and proxy map based on U.S. public sources (hazard indices, insurance market indicators, HMDA origination structure, house price dynamics, and consumer stress/complaint measures) and propose robustness checks addressing pre-trends, regional confounders, and distributional heterogeneity. To make the manuscript submission-ready, we include clearly labeled demonstration results generated on a simulated county-year panel calibrated to plausible ranges; these are placeholders that can be replaced with empirical estimates once the merged dataset is assembled. Policy implications focus on governance baselines for InsurTech-driven risk signals, transparency standards for insurance frictions, targeted consumer protections, and resilience metrics for housing finance intermediaries. The paper contributes a hybrid conceptual–methodological template enabling regulators, lenders, insurers, and FinTech platforms to measure and mitigate climate-driven insurance gaps before they translate into mortgage distress and broader household credit fragility.
climate resilience; InsurTech; homeowners insurance; non-renewals; mortgage delinquency; household credit risk; consumer protection; financial stability; governance; U.S. housing finance
The Use of Technology and Data Analytics in Modern Auditing: A Systematic Review
The auditing profession is undergoing a significant transformation driven by advancements in technology and data analytics. While these innovations promise enhanced audit quality, efficiency, and reliability, their adoption and effectiveness vary across contexts. This systematic review synthesizes empirical evidence from 2020 to 2025 to examine the types, applications, outcomes, and challenges of technology and data analytics in modern auditing. Following PRISMA guidelines, a comprehensive search was conducted across Scopus, Web of Science, IEEE Xplore, and ScienceDirect, yielding 260 records. After screening, 10 studies met the inclusion criteria. Risk of bias was assessed using the ROBINS-I tool, and findings were synthesized narratively due to methodological heterogeneity. The review highlights the transformative impact of technologies such as big data analytics, artificial intelligence, and federated learning on audit quality and efficiency. Key findings include the positive role of client technological readiness in remote auditing, the moderating effect of cybersecurity on audit data analytics, and the challenges of skill gaps and resource constraints in small and medium-sized practices. However, benefits are context-dependent, with emerging markets facing unique regulatory and infrastructural barriers. While technology and data analytics offer substantial benefits for auditing, their successful implementation requires tailored strategies that address contextual, technical, and human factors. Future research should prioritize longitudinal and comparative studies to bridge the gap between experimental promise and practical application.
auditing, technology, data analytics, artificial intelligence, systematic review, audit quality
A study of Implicit disagreement in MPC meetings for selected Eastern European Economies using the Taylor Rule
This paper in an empirical attempt to study implicit disagreement among MPC members of central banks contained in the minutes of the MPC meetings for three Eastern European economies, viz., Poland, Czech Republic and Hungary. NLP sentiment analysis techniques, specifically a BERT model trained and fine-tuned on the corpus of the text of the minutes of the MPC meetings of the three countries, are applied to extract sentence level sentiment scores in terms of hawkish, dovish or neutral sentiment which are then used to calculate meeting level Disagreement Index(DI) variable by taking the Standard Deviation of the sentence level sentiment scores. We augment the standard Taylor rule formula for setting monetary policy rates with this DI variable and see that it impacts the interest rate negatively, suggesting that higher implicit disagreement might restrain the MPC members from being too hawkish which might be influenced by higher uncertainty about economic conditions, especially fears of a looming recession. So, we find that disagreement has a significant impact, even implicit disagreement, on the policy rate decisions of the MPC members. This offers insights into why central banks might hesitate to raise rates immediately in face of higher inflation or output gap pressures.
A Deterministic Trajectory-Level Evaluation Framework for Learning-Based Agentic Systems
In the recent past, learning-based agentic systems are being increasingly used to tackle complex decision environments where reliability, transparency, and compliance with governance are key. However, the dominant evaluation strategies are mostly outcome-centric, with a focus on aggregate performance measures like accuracy, rewards, or task completion rates. These evaluation strategies provide little information on the internal decision-making processes that lead to the observable outcomes, especially for reasoning-based agentic systems with multiple steps. This paper proposes a Deterministic Trajectory Level Evaluation Framework (DTLEF) for learning-based agentic systems. The proposed framework transforms the evaluation paradigm from traditional outcome-oriented metrics to an evaluation process that focuses on the trajectories of states and actions within controlled execution conditions. The DTLEF integrates standardized states, comprehensive action trace logging, deterministic replay validation, and behavior verification against governance constraints. The evaluation process, in which the agentic system runs in controlled inference mode and the trajectory traces are compared, enables the identification of policy instabilities, reasoning drift, and non-deterministic behavior. Moreover, the evaluation process also ensures that the trajectories are consistent with the predefined constraints. Unlike traditional performance metrics, the proposed framework does not depend upon empirical data or domain-specific metrics. Instead, it provides a methodology for evaluating agentic systems, including autonomous decision pipelines, tool-enhanced language agents, and cyber-physical control systems, at an architecture level. The proposed framework increases transparency, reproducibility, and compliance without modifying training procedures. This research provides a scalable and domain-independent evaluation methodology for validating learning-based autonomous agents in environments where behavioral reliability is as important as functional performance by formally defining trajectory-level determinism as a primary evaluation criterion.
Agentic Systems, Trajectory-Level Evaluation, Deterministic Replay, Learning-Based Autonomous Agents, AI Governance and Reliability
Impact of ERP-Enabled Digital Transformation on Cost Accounting Practices in Saudi Firms
Enterprise Resource Planning (ERP) systems are major instruments of organizational digital transformation as they integrate operational and financial processes and uplift the overall performance. This research focuses on the effect of ERP implementation on cost accounting activities in Saudi firms, the mediating roles of process integra-tion and automation being the primary emphasis. Information was obtained from 60 companies with a total of 290 employees from the manufacturing, construction, retail, energy, and service sectors. Reliability and validity were established through Cronbachs alpha, composite reliability, and average variance extracted. For data analysis, multiple regression, independent t, tests, and Partial Least Squares Structural Equation Modeling were utilized. Findings suggest that the adoption of ERP leads to a significant improve-ment in cost accuracy, cost control effectiveness, variance analysis quality, and managerial decision, making. In addition, process integration and automation partially mediate these relationships, thus advocating the strategic ERP adoption aligned with Saudi Vision 2030 goals.
Cost Accounting, ERP, Digital Transformation, Integration, cost accuracy, cost control effectiveness, variance analysis quality, managerial decision making
Machine Learning Models for Detecting Hidden Collusion Networks in U.S. Corporate Finance
Hidden collusion networks in U.S. corporate finance present a significant challenge to market integrity and regulatory oversight. Such networks are difficult to detect due to the indirect and distributed nature of collusive behavior, which is often embedded within legitimate financial transactions and governance relationships. This study aims to develop and evaluate machine learning models capable of detecting latent collusion structures by combining firm-level financial indicators with relational and governance-based network information. The goal is to uncover clusters of firms that exhibit coordinated behavior while providing interpretable insights for regulatory decision-making. A multi-stage detection framework was implemented, incorporating classical machine learning classifiers, tree-based ensembles, graph neural networks (GNNs), and hybrid models combining feature-driven and structure-driven learning. Corporate entities were represented as nodes within financial and governance networks, with edges encoding ownership ties, shared executives, transactional dependencies, and temporal co-movements. Models were evaluated using precision, recall, F1-score, AUC-ROC, AUC-PR, and network consistency metrics, while robustness analyses examined performance under class imbalance and sparse labeling conditions. Graph-based models outperformed traditional baselines, achieving F1-scores up to 0.91 and AUC-ROC values up to 0.94. Hybrid ensembles that combined tree-based and graph-based predictions achieved the highest overall performance (F1-score = 0.93, AUC-ROC = 0.95, AUC-PR = 0.91). The models successfully identified densely connected collusive clusters and key hub firms, highlighting the importance of relational and temporal features over isolated firm-level indicators. Ablation studies confirmed that financial metrics alone were insufficient to detect coordinated behavior without network structure. The study demonstrates that machine learning, particularly when integrated with graph-based relational representations, provides an effective and scalable approach for detecting hidden collusion networks in corporate finance. The proposed framework offers practical value for regulators, enabling probabilistic risk assessment, early-warning detection, and data-driven surveillance of potentially collusive activity.
Copyright (c) 2024 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Real-Time Payments and Real-Time Fraud: A U.S. FinTech Risk Framework for RTP Rails and Consumer Protection
Real-time payment (RTP) rails convert retail payments from a batch, revocable process into an always-on, irrevocable, data-rich transfer of value. In the United States, this transition accelerated during 2012–2023 as mobile wallets, account-to-account (A2A) transfers, and instant credit transfers expanded, culminating in nationwide rail competition between private-sector networks and the Federal Reserve’s FedNow Service. The same design features that make RTP economically attractive—immediacy, finality, and API-based integration—also magnify fraud externalities and operational-resilience demands. Authorized push payment (APP) scams exploit consumer initiation and social engineering, while mule accounts, synthetic identities, and rapid cash-out compress detection windows from days to seconds. Regulatory liability regimes (e.g., U.S. Regulation E) were built around unauthorized transfers and do not fully internalize APP harms, creating incentives that may underinvest in preventive controls. This paper develops a pragmatic–institutional risk framework for U.S. RTP ecosystems that integrates (i) fraud typologies and externalities, (ii) operational-resilience requirements for 24/7 settlement, and (iii) consumer-protection and governance mechanisms spanning federal and state authorities. Methodologically, we specify a replicable measurement architecture using a simulated institution–region–year panel (2012–2023) that combines RTP intensity, fraud loss proxies, incident-response performance, and governance strength indicators. We outline identification strategies suitable for U.S. data constraints—fixed-effects panels, staggered adoption difference-in-differences, and instrumented uptake using broadband penetration and legacy payment frictions. Illustrative estimates show that RTP penetration is associated with a statistically meaningful rise in reported scam losses and complaint intensity in early adoption phases, while institutions with stronger control stacks—payee confirmation, transaction risk scoring, mule-network interdiction, and real-time case management—attenuate fraud growth and reduce tail operational incidents. We translate these findings into a U.S.-specific governance blueprint that aligns rail operators, depository institutions, nonbank PSPs, and regulators around a shared ‘prevent–detect–respond’ control taxonomy, standardized reporting metrics, and consumer redress principles. The framework supports policy goals of safer faster payments without suppressing innovation, and is designed to be extendable to cross-border instant payments dialogues. Research highlights. This paper develops a US-focused risk framework for real-time payments (RTP) rails and consumer protection across 2012–2023, bridging payment-systems design, fraud economics, and institutional governance. It clarifies why fast settlement increases the value of speed for legitimate users and for offenders, and shows how that shared incentive creates externalities that a single provider cannot fully price or internalize. Using a simulated institution–region–year panel, the study provides a replicable method for measuring (i) fraud externalities (losses shifted across consumers, banks, merchants and platforms), (ii) control effectiveness (prevention, detection, and response), and (iii) operational resilience (availability and recovery under stress). The results emphasize that the same features that make RTP socially valuable—immediacy, irrevocability, 24/7 availability—also intensify the downside of weak authentication, weak dispute design, and fragmented oversight. Policy-wise, the paper offers a coordinated control stack aligned to US agencies and industry bodies and proposes practical, auditable metrics (e.g., APP loss rate per 10k transactions, false-positive friction rate, time-to-recall, time-to-freeze, and recovery SLA compliance). The governance contribution is a ‘rails-and-responsibilities’ map that connects economic function to supervisory responsibility, supporting activity-based regulation and reducing arbitrage across state and federal regimes.
Real-time payments; FedNow; RTP network; authorized push payment scams; fraud externalities; operational resilience; consumer protection; U.S. financial regulation; payment governance
Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability
Financial technology (FinTech) has become a core component of the United States financial system, reshaping payment infrastructure, consumer credit markets, and data-driven financial intermediation. While FinTech innovation has improved efficiency and expanded financial access, its rapid scaling has also introduced new sources of systemic risk, regulatory fragmentation, and consumer vulnerability—particularly as artificial intelligence increasingly governs financial decision-making. This study examines FinTech innovation through an integrated governance lens, focusing on how AI-enabled intermediation interacts with financial stability and public-interest outcomes in the United States. Using a multi-source, institutionally grounded panel dataset spanning 2012–2022, the analysis conceptualizes FinTech as part of national financial infrastructure rather than as a peripheral technological disruption. The empirical strategy combines a composite measure of FinTech intensity with indicators of explainable AI adoption and institutional governance strength to assess their joint effects on financial stability risk and public-interest performance. Fixed-effects panel models, interaction specifications, and robustness checks are employed to isolate institutional and temporal dynamics. The results reveal three central findings. First, greater FinTech intensity enhances intermediation efficiency and financial inclusion but is associated with elevated short-term financial stability risks when deployed without adequate oversight. Second, the adoption of explainable AI significantly moderates these risks by reducing algorithmic opacity and improving auditability and supervisory visibility. Third, governance capacity—reflected in regulatory coordination, disclosure mandates, and supervisory engagement—emerges as a decisive factor in aligning technological innovation with systemic resilience and consumer trust. These findings suggest that the principal challenge facing the U.S. financial system is not technological innovation itself, but the institutional lag between innovation and governance. By demonstrating that explainable AI functions as a stabilizing mechanism rather than merely a compliance tool, the study advances both financial stability theory and the literature on AI governance in finance. The paper contributes a conceptual–methodological framework that integrates innovation, governance, and public interest, offering actionable policy insights for regulators, financial institutions, and FinTech firms. More broadly, the analysis underscores the importance of governing AI-enabled finance in a manner that supports long-term financial stability, equity, and national economic resilience.
FinTech, artificial intelligence, financial stability, governance, algorithmic accountability, consumer protection, United States